Echo state network with a global reversible autoencoder for time series classification

DOI10.1016/j.ins.2021.04.074OpenAlexW3157786233MaRDI QIDQ6092053

Q. M. Jonathan Wu, Yimin Yang, Jianbin Xin, Kunjie Yu, Dongshu Wang, Heshan Wang

Publication date: 23 November 2023

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2021.04.074

zbMATH Keywords

recurrent neural networktime series classificationecho state networkglobal reversible autoencoder

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Classification of time series by shapelet transformation
• A time series forest for classification and feature extraction
• CID: an efficient complexity-invariant distance for time series
• Combining discrete SVM and fixed cardinality warping distances for multivariate time series classification
• Time series classification with ensembles of elastic distance measures
• The BOSS is concerned with time series classification in the presence of noise
• Using dynamic time warping distances as features for improved time series classification
• Time series representation and similarity based on local autopatterns
• Understanding autoencoders with information theoretic concepts
• Time series classification with echo memory networks
• Early classification of time series using multi-objective optimization techniques
• Functional echo state network for time series classification
• Nearest neighbor pattern classification
• Analysis and Design of Echo State Networks

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